配电网高次谐波抑制方法的分析

为在配电网中实施有效的谐波抑制,就系统极点及其灵敏度分析法、模拟退火法与遗传算法等３种方法作了比较、分析。着重对遗传算法作了联系实际的剖析,成功地应用了自适应遗传算法,以克服传统谐波分析方法的盲目性,合理地优化滤波器在网络中的配置,以较少的投资获得最佳的网络谐波综合抑制效果。     

基于自适应遗传算法的风力发电系统网侧LCL滤波器的优化设计

大功率风力发电系统常在电网侧接入滤波器以抑制变流器带来的高频谐波,但LCL滤波器参数的设计仍存在计算复杂、准确性低、滤波性能差等不足。提出了基于自适应遗传算法来优化设计网侧LCL滤波器参数。通过采用自适应的交叉概率和变异概率来提高进化效率以减小参数优化计算时间,并综合考虑LCL滤波器的重要约束条件和滤波性能来优化LCL滤波器的主要参数。最后PSCAD/EMTDC的实例仿真结果充分表明采用优化参数的滤波器具有良好的网侧谐波抑制能力,为工程应用提供了重要的依据。     

基于改进型遗传算法的多谐波源配电网的谐波分析

在配电网络中,由于馈电线的投切、补偿电容器的分组投切和负荷阻抗的变化,以及多个谐波源的大小与相位的动态变化,给网络中谐波电压的估算和滤波器的设置带来困难。结合简单遗传算法在方法上存在的不足,提出了改进型的自适应遗传算法,并应用改进型的遗传算法进行配电网络的谐波分析,找出以上各种情况下谐波污染最严重的情况,给谐波的治理带来方便和可靠的技术手段,应用实例是合适的。     

基于改进型遗传算法的多谐波源配电网的谐波分析

在配电网络中,由于馈电线的投切、补偿电容器的分组投切和负荷阻抗的变化,以及多个谐波源的大小与相位的动态变化,给网络中谐波电压的估算和滤波器的设置带来困难。结合简单遗传算法在方法上存在的不足,提出了改进型的自适应遗传算法,并应用改进 遗传算法进行配电网络的谐波分析,找出以上各种情况下谐波污染最严重的情况,给谐波的治理带来方便和可靠的技术手段,实例是合适的。     

配电网的谐波源特性与高次谐波的抑制

结合配电网实际 ,阐述了配电网络中高次谐波产生的原因与常见谐波源的特性 ,对其干扰与危害作了分析。介绍常用的高次谐波抑制方法 ,并较详细地就无源滤波器的种类和抑制机理作了探讨 ,而且对正处于研制和一定规模应用的有源滤波器的特点作了简要介绍。以帮助广大工程技术人员对谐波和抑制措施有一个较全面的概念和认识     

配电网的谐波源特性与高次谐波的抑制

结合配电网实际 ,阐述了配电网络中高次谐波产生的原因与常见谐波源的特性 ,对其干扰与危害作了分析。介绍常用的高次谐波抑制方法 ,并较详细地就无源滤波器的种类和抑制机理作了探讨 ,而且对正处于研制和一定规模应用的有源滤波器的特点作了简要介绍。以帮助广大工程技术人员对谐波和抑制措施有一个较全面的概念和认识     

改进遗传算法和神经网络在电能质量扰动识别中的应用

提出了一种改进遗传算法(IGA).该算法采取了有效的选择和交叉策略,设计了自适应变化的交叉概率和变异概率,以及随进化代数自适应变化的移民算法.该改进遗传算法稳定性和收敛性能较好,将其应用到基于BP网络的电能质量扰动识别中,提高了识别的稳定性和收敛性能,并使基于BP网络的电能质量扰动识别系统更具实用性.文中最后通过仿真计算验证了本文方法的性能.     

自适应遗传算法在配电网谐波分析中的应用

在配电网络中,由于馈电线的投切、补偿电容器的分组投切和负荷阻抗的变化,以及多个谐波源的大小与相位的动态变化,给网络中谐波电压的估算和滤波器的设置带来困难,文章结合简单遗传算法的基本原理,应用自适应型的遗传算法进行配电网络的谐波分析,找出以上各种情况下谐波污染最严重的情况,给谐波的治理带来方便和可靠的技术手段.     

基于改进自适应遗传算法的配电网络重构

提出了一种用于配电系统网络重构的改进型自适应遗传算法。给出了网络重构问题的数学模型及改进的自适应遗传算法。在应用遗传算法时结合配电网自身的特点,提出以环路开关号为基因、系统环路数为染色体长度的编码方法,在优化过程中采用自适应调整的交叉率和变异率,结合一定的禁忌规则．较好地提高了算法在网络重构方面的效率。在IEEE16节点、33节点、69节点3个不同规模的算例系统上进行了测试,计算结果表明,所提出的方法缩短了染色体长度,较好地抑制了不可行解的产生．无论是在收敛性、稳定性还是在计算效率上都取得了比较满意的结果。     

基于自适应遗传算法的配电网络重构

建立了兼顾经济性和可靠性的多目标配电网络重构数学模型，采用自适应遗传算法进行求解，根据个体适应度的变化自动调整交叉与变异概率，克服了遗传算法易早熟的缺点，加快了算法寻优速度。通过仿真算例分析比较，该方法是可行有效的。     

基因/禁忌组合算法在配电网网架优化规划中的应用

在比较了基因算法和禁忌算法各自优缺点的基础上,针对配电网网架优化规划中多约束、非线性和整数寻优的特点,提出采用基因／禁忌组合的算法的策略,并应用于配电网网架优化规划。算法计算证明了该组合算法在配电网网架优化规划中应用的可行性和优越性。     

基于随机生成树策略的配网重构遗传算法

常规遗传算法求解配电网重构不能保证配电网的辐射状结构,会带来大量不可行解,而现有的改进编码方法和遗传策略操作复杂、计算量大、对复杂配电网并不十分有效。为了解决上述问题,引入了随机生成树策略,即根据支路序号的实数编码方式产生随机序列,再利用图论的"避圈法"产生对应的生成树,确保每个重构方案都是树状结构,使遗传进化在解空间内连续进行;同时为避免早熟,提高收敛速度,在变异操作中动态控制变异率。33节点和69节点典型算例验证了方法的有效性和良好的计算性能,表明方法求解迅速、简单有效。     

Optimal DG Allocation in Radial Distribution Systems with High Penetration of Non-linear Loads

This paper addresses the optimal distributed generation sizing and siting for voltage profile improvement, power losses, and total harmonic distortion (THD) reduction in a distribution network with high penetration of non-linear loads. The proposed planning methodology takes into consideration the load profile, the frequency spectrum of non-linear loads, and the technical constraints such as voltage limits at different buses (slack and load buses) of the system, feeder capacity, THD limits, and maximum penetration limit of DG units. The optimization process is based on the Genetic Algorithm (GA) method with three scenarios of objective function: system power losses, THD, and multi-objective function-based power losses and THD. This method is executed on the IEEE 31-bus system under sinusoidal and non-sinusoidal (harmonics) operating conditions including load variations within the 24-hr period. The simulation results using Matlab environment show the robustness of this method in optimal sizing and siting of DG, efficiency for improvement of voltage profile, reduction of power losses, and THD. A comparison with particle swarm optimization (PSO) method shows that the proposed method is better than PSO in reducing the power losses and THD in all suggested scenarios.     

Novel FGbSA: Fuzzy-Galaxy-based search algorithm for multi-objective reconfiguration of distribution systems

Reconfiguration according to different criteria is an important problem in distribution systems. This paper presents a new method for optimal multi-objective reconfiguration of distribution system based on the Galaxy-based Search Algorithm (GbSA). To avoid the convergence problem, the input and output data are normalized in the same range using fuzzy sets. The main objectives of the proposed algorithm have been considered as power loss reduction, voltage profile improvement and increase of the system load balancing. The proposed technique has been investigated using the IEEE 33-bus test system and a real distribution network i.e. Tai-Power 11.4-kV distribution system. The obtained results revealed the superiority of the proposed fuzzy-GbSA method in terms of accuracy compared to the GbSA and other intelligent search algorithms such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO). Furthermore, the proposed algorithm efficiently converged to the optimum solution compared to the other intelligent counterpart algorithms.     

遗传算法在配电网停电恢复中的应用

将遗传算法应用于配电网停电恢复中，给出了综合考虑开关操作次数最少，网损最小以及潮流分布合理等方面的停电恢复模型，采用遗虎法对停电区域进行重构，形成恢复方案，通过对实际配电网的测试分析表明，将遗传算法用于电网停电恢复中，能快速有效地提供若干个优化方案，为运行人员提供更多的参考。     

Hybrid optimization implemented for distributed generation parameters in a power system network

This paper presents a novel hybrid optimization algorithm for optimizing the Distributed Generation (DG) parameters in deregulated power system which improves the stability, reduces the losses and also increases the cost of generation. This Hybrid algorithm which includes Fuzzy-Genetic Algorithm (FGA) is used to optimize the various DG parameters simultaneously. The various parameters taken into consideration are their type, location and size of the DG devices. The simulation was performed on a distribution system and modeled for steady state studies. The optimization results are compared to the solution given by another search method like Genetic Algorithm (GA) and Micro Genetic Algorithm (MGA). The results reveal the benefits of the proposed method, for solving simultaneous combinorial problems of DG devices in a power system network.     

Neuro-evolutionary approaches to power system harmonics estimation

This paper focuses on exploiting two computational intelligence techniques such as artificial neural network and evolutionary computation techniques in estimation of harmonics in power system. Accurate estimation of harmonics in distorted power system current/voltage signal is essential to effectively design filters for eliminating harmonics. No standard design is available for handling of local minima and training of NN but Evolutionary Computation (EC) techniques are capable of resolving local minima. Neural Network and Evolutionary Computing (Bacterial Foraging Optimization (BFO)) are combined to achieve accurate estimation of different harmonics components of a distorted power system signal. First estimation of unknown parameters are carried out using BFO, then optimized output of BFO are taken as initial values of the unknown parameters for Adaline. Amplitude and phase of fundamental and harmonics components are determined from final updated values of unknown parameters using Adaline. This Adaline based Bacterial Foraging Optimization (Adaline-BFO) hybrid estimation algorithm addresses the problems of slow convergence and reduction of time generation of off-springs happening in Genetic Algorithm (GA), and to avoid local minima in Particle Swarm Optimization (PSO). The proposed Adaline-BFO algorithm has been applied for estimation of harmonics of the voltage obtained across the inverter terminals of a prototype Photovoltaic (PV) system. From the obtained results, it is confirmed that the proposed Adaline-BFO algorithm provides superior estimation performance in terms of improvement in % error in estimation, processing time in computation and performance in presence of inter and sub-harmonic components when compared with the Discrete Fourier Transform (DFT), Kalman Filter (KF) and BFO algorithms.     

考虑TSC提升的配电网区域机会维修模型

基于状态检修中设备性能要求与系统可靠性要求之间的矛盾,在传统状态检修的基础上,提出一种考虑最大供电能力(TSC)提升的配电网区域机会维修模型。提出一种区域机会维修模型以确定检修区域,该模型综合考虑设备个体性能、系统可靠性要求、设备间的关联、系统网络结构等关键因素,在此基础上,提出一种以TSC提升为目标函数的负荷转移模型,该模型通过节点深度编码(NDE)技术与遗传算法(GA)相结合的优化算法优化网络结构来得到满意的重构网络。以IEEE 33节点系统为例验证了所提算法提升TSC的有效性,以RBTS Bus2可靠性测试系统为例验证了考虑TSC提升的区域机会维修模型的有效性、实用性和科学性。     

基于单亲遗传算法的配电网络规划

配电网络规划属于非线性混合整数规划问题，应用传统数学优化算法往往难以直接求解，文章针对目前应用广泛的常规遗传算法在求解该问题时存在的难以保证方案连通性和辐射性的缺陷，提出了基于单亲遗传算法的配电网络优化规划算法，于整数编码策略，给出了配电网络规划中变量编码的具体方法和迭代求解程序，该算法具有进化操作成功率高，求解配电网络优化规划问题的效率高等优点，同时可将简化网架结构和选取导线截面结合在一起，既可确保解的最优性，又可减少工作量，仿真算例验证了该方法的快速性和有效性，该算法还适用于辐射型配电网络的扩展规划和配电网络的重构。